By Luiz DeRose, Bronis R. de Supinski, Stephen L. Olivier, Barbara M. Chapman, Matthias S. Müller
This publication constitutes the refereed complaints of the tenth overseas Workshop on OpenMP, held in Salvador, Brazil, in September 2014. The sixteen technical complete papers provided have been conscientiously reviewed and chosen from 18 submissions. The papers are prepared in topical sections on tasking types and their optimization; realizing and verifying correctness of OpenMP courses; OpenMP reminiscence extensions; extensions for instruments and locks; reports with OpenMP machine constructs.
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Plasma on INTEL32 Table 1. 54 the sub domain of the initial grid. One of the important problem is that tasks are not bound to resources in order to take data locality into account. Typical scenario is that tasks between successive iterations may be performed by different threads of the same parallel region. Neither the CLANG or the GCC runtime tries to schedule tasks in order to maximize data reuse. Moreover, tasks that access to same data (due to sharing of frontiers between two sub-domains) may be better scheduled if they are mapped to cores on the same NUMA node.
StarPU provides ways of splitting a data with user-defined filters that can help to express dependencies on sub-matrices, for example. The Quark runtime system that comes with the PLASMA library is responsible for executing tasks created out of BLAS operators in a dynamic way. Unlike KAAPI and StarPU, Quark only considers unidimensional arrays, but comes with an original scratch access mode to reuse thread-specific temporary data. OMPSs  is a programming model inspired by OpenMP with specific directives to support task dependencies and heterogeneity.
S. ) IWOMP 2013. LNCS, vol. 8122, pp. 84–98. Springer, Heidelberg (2013)  OpenMP Architecture Review Board. : Beyond nested parallelism: Tight bounds on work-stealing overheads for parallel futures. ) SPAA, pp. 91–100. se Abstract. 0). This paradigm simpliﬁes the writing of parallel applications, extracting parallelism, and facilitates the use of distributed memory architectures. While the programming model itself is becoming mature, a problem with current run-time scheduler implementations is that they require a very large task granularity in order to scale.